The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.

Text categorization methods for automatic estimation of verbal intelligence

In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.

Distributed bees algorithm for task allocation in swarm of robots

In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.

ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

Structural changes after videogame practice related to a brain network associated with intelligence

Here gray and white matter changes after four weeks of videogame practice were analyzed using optimized voxel-based morphometry (VBM), cortical surface and cortical thickness indices, and white matter integrity computed from several projection, commissural, and association tracts relevant to cognition. Beginning with a sample of one hundred young females, twenty right handed participants were recruited for the study and assigned to a practice or a control group carefully matched by their general cognitive ability scores. After the first scan, the practice group played ‘Professor Layton and The Pandora's Box’ 4 h per week during four weeks. A second scan was obtained at the end of practice and intelligence was measured again. Image analyses revealed gray and white matter changes in the practice group. Gray matter changes theoretically relevant for intelligence were observed for the practice group mainly in frontal clusters (Brodmann areas 9 and 10) and also in smaller parietal and temporal regions. White matter findings were focused in the hippocampal cingulum and the inferior longitudinal fasciculus. These gray and white matter changes presumably induced by practice did not interact with intelligence tests' scores.

Estimating Adaptacion of Dialogue Partners with Different Verbal Intelligence

This work investigates to what degree speakers with different verbal intelligence may adapt to each other. The work is based on a corpus consisting of 100 descriptions of a short film (monologues), 56 discussions about the same topic (dialogues), and verbal intelligence scores of the test participants. Adaptation between two dialogue partners was measured using cross-referencing, proportion of "I", "You" and "We" words, between-subject correlation and similarity of texts. It was shown that lower verbal intelligence speakers repeated more nouns and adjectives from the other and used the same linguistic categories more often than higher verbal intelligence speakers. In dialogues between strangers, participants with higher verbal intelligence showed a greater level of adaptation.

Investigating verbal intelligence using the TF-IDF approach

In this paper we investigated differences in language use of speakers yielding different verbal intelligence when they describe the same event. The work is based on a corpus containing descriptions of a short film and verbal intelligence scores of the speakers. For analyzing the monologues and the film transcript, the number of reused words, lemmas, n-grams, cosine similarity and other features were calculated and compared to each other for different verbal intelligence groups. The results showed that the similarity of monologues of higher verbal intelligence speakers was greater than of lower and average verbal intelligence participants. A possible explanation of this phenomenon is that candidates yielding higher verbal intelligence have a better short-term memory. In this paper we also checked a hypothesis that differences in vocabulary of speakers yielding different verbal intelligence are sufficient enough for good classification results. For proving this hypothesis, the Nearest Neighbor classifier was trained using TF-IDF vocabulary measures. The maximum achieved accuracy was 92.86%.

Relating dominance of dialogue participants with their verbal intelligence scores

In this work we investigated whether there is a relationship between dominant behaviour of dialogue participants and their verbal intelligence. The analysis is based on a corpus containing 56 dialogues and verbal intelligence scores of the test persons. All the dialogues were divided into three groups: H-H is a group of dialogues between higher verbal intelligence participants, L-L is a group of dialogues between lower verbal intelligence participant and L-H is a group of all the other dialogues. The dominance scores of the dialogue partners from each group were analysed. The analysis showed that differences between dominance scores and verbal intelligence coefficients for L-L were positively correlated. Verbal intelligence scores of the test persons were compared to other features that may reflect dominant behaviour. The analysis showed that number of interruptions, long utterances, times grabbed the floor, influence diffusion model, number of agreements and several acoustic features may be related to verbal intelligence. These features were used for the automatic classification of the dialogue partners into two groups (lower and higher verbal intelligence participants); the achieved accuracy was 89.36%.

An introduction to swarm robotics

Swarm robotics is a field of multi-robotics in which large number of robots are coordinated in a distributed and decentralised way. It is based on the use of local rules, and simple robots compared to the complexity of the task to achieve, and inspired by social insects. Large number of simple robots can perform complex tasks in a more efficient way than a single robot, giving robustness and flexibility to the group. In this article, an overview of swarm robotics is given, describing its main properties and characteristics and comparing it to general multi-robotic systems. A review of different research works and experimental results, together with a discussion of the future swarm robotics in real world applications completes this work.

Increasing the Intelligence of Virtual Sales Assistants through Knowledge Modeling Techniques

Shopping agents are web-based applications that help consumers to find appropriate products in the context of e-commerce. In this paper we argue about the utility of advanced model-based techniques that recently have been proposed in the fields of Artificial Intelligence and Knowledge Engineering, in order to increase the level of support provided by this type of applications. We illustrate this approach with a virtual sales assistant that dynamically configures a product according to the needs and preferences of customers.

Identification of intermediate debonding damage in FRP-plated RC beams based on multi-objective particle swarm optimization without updated baseline model

Fiber reinforced polymer composites (FRP) have found widespread usage in the repair and strengthening of concrete structures. FRP composites exhibit high strength-to-weight ratio, corrosion resistance, and are convenient to use in repair applications. Externally bonded FRP flexural strengthening of concrete beams is the most extended application of this technique. A common cause of failure in such members is associated with intermediate crack-induced debonding (IC debonding) of the FRP substrate from the concrete in an abrupt manner. Continuous monitoring of the concrete?FRP interface is essential to pre- vent IC debonding. Objective condition assessment and performance evaluation are challenging activities since they require some type of monitoring to track the response over a period of time. In this paper, a multi-objective model updating method integrated in the context of structural health monitoring is demonstrated as promising technology for the safety and reliability of this kind of strengthening technique. The proposed method, solved by a multi-objective extension of the particle swarm optimization method, is based on strain measurements under controlled loading. The use of permanently installed fiber Bragg grating (FBG) sensors embedded into the FRP-concrete interface or bonded onto the FRP strip together with the proposed methodology results in an automated method able to operate in an unsupervised mode.

Advances in technologies and techniques for ambient intelligence

Human activity attracting a lot of research activity in several fields including the use of wireless sensors, positioning technologies and techniques, embedded computing, remote sensing and energy management among others. There are a number of applications where the results of those investigations can be applied, including ambient intelligence to support human activity, particularly the elderly and disabled people. Ambient intelligence is a new paradigm for the information and communications technologies where the electronic/digital environment takes care of the people presence and their needs, becoming an active, adaptive and responsive environment.

A Personal Agent Architecture for Task Automation in the Web of Data. Bringing intelligence to everyday tasks

Internet está evolucionando hacia la conocida como Live Web. En esta nueva etapa en la evolución de Internet, se pone al servicio de los usuarios multitud de streams de datos sociales. Gracias a estas fuentes de datos, los usuarios han pasado de navegar por páginas web estáticas a interacturar con aplicaciones que ofrecen contenido personalizado, basada en sus preferencias. Cada usuario interactúa a diario con multiples aplicaciones que ofrecen notificaciones y alertas, en este sentido cada usuario es una fuente de eventos, y a menudo los usuarios se sienten desbordados y no son capaces de procesar toda esa información a la carta. Para lidiar con esta sobresaturación, han aparecido múltiples herramientas que automatizan las tareas más habituales, desde gestores de bandeja de entrada, gestores de alertas en redes sociales, a complejos CRMs o smart-home hubs. La contrapartida es que aunque ofrecen una solución a problemas comunes, no pueden adaptarse a las necesidades de cada usuario ofreciendo una solucion personalizada. Los Servicios de Automatización de Tareas (TAS de sus siglas en inglés) entraron en escena a partir de 2012 para dar solución a esta liminación. Dada su semejanza, estos servicios también son considerados como un nuevo enfoque en la tecnología de mash-ups pero centra en el usuarios. Los usuarios de estas plataformas tienen la capacidad de interconectar servicios, sensores y otros aparatos con connexión a internet diseñando las automatizaciones que se ajustan a sus necesidades. La propuesta ha sido ámpliamante aceptada por los usuarios. Este hecho ha propiciado multitud de plataformas que ofrecen servicios TAS entren en escena. Al ser un nuevo campo de investigación, esta tesis presenta las principales características de los TAS, describe sus componentes, e identifica las dimensiones fundamentales que los defines y permiten su clasificación. En este trabajo se acuña el termino Servicio de Automatización de Tareas (TAS) dando una descripción formal para estos servicios y sus componentes (llamados canales), y proporciona una arquitectura de referencia. De igual forma, existe una falta de herramientas para describir servicios de automatización, y las reglas de automatización. A este respecto, esta tesis propone un modelo común que se concreta en la ontología EWE (Evented WEb Ontology). Este modelo permite com parar y equiparar canales y automatizaciones de distintos TASs, constituyendo un aporte considerable paraa la portabilidad de automatizaciones de usuarios entre plataformas. De igual manera, dado el carácter semántico del modelo, permite incluir en las automatizaciones elementos de fuentes externas sobre los que razonar, como es el caso de Linked Open Data. Utilizando este modelo, se ha generado un dataset de canales y automatizaciones, con los datos obtenidos de algunos de los TAS existentes en el mercado. Como último paso hacia el lograr un modelo común para describir TAS, se ha desarrollado un algoritmo para aprender ontologías de forma automática a partir de los datos del dataset. De esta forma, se favorece el descubrimiento de nuevos canales, y se reduce el coste de mantenimiento del modelo, el cual se actualiza de forma semi-automática. En conclusión, las principales contribuciones de esta tesis son: i) describir el estado del arte en automatización de tareas y acuñar el término Servicio de Automatización de Tareas, ii) desarrollar una ontología para el modelado de los componentes de TASs y automatizaciones, iii) poblar un dataset de datos de canales y automatizaciones, usado para desarrollar un algoritmo de aprendizaje automatico de ontologías, y iv) diseñar una arquitectura de agentes para la asistencia a usuarios en la creación de automatizaciones. ABSTRACT The new stage in the evolution of the Web (the Live Web or Evented Web) puts lots of social data-streams at the service of users, who no longer browse static web pages but interact with applications that present them contextual and relevant experiences. Given that each user is a potential source of events, a typical user often gets overwhelmed. To deal with that huge amount of data, multiple automation tools have emerged, covering from simple social media managers or notification aggregators to complex CRMs or smart-home Hub/Apps. As a downside, they cannot tailor to the needs of every single user. As a natural response to this downside, Task Automation Services broke in the Internet. They may be seen as a new model of mash-up technology for combining social streams, services and connected devices from an end-user perspective: end-users are empowered to connect those stream however they want, designing the automations they need. The numbers of those platforms that appeared early on shot up, and as a consequence the amount of platforms following this approach is growing fast. Being a novel field, this thesis aims to shed light on it, presenting and exemplifying the main characteristics of Task Automation Services, describing their components, and identifying several dimensions to classify them. This thesis coins the term Task Automation Services (TAS) by providing a formal definition of them, their components (called channels), as well a TAS reference architecture. There is also a lack of tools for describing automation services and automations rules. In this regard, this thesis proposes a theoretical common model of TAS and formalizes it as the EWE ontology This model enables to compare channels and automations from different TASs, which has a high impact in interoperability; and enhances automations providing a mechanism to reason over external sources such as Linked Open Data. Based on this model, a dataset of components of TAS was built, harvesting data from the web sites of actual TASs. Going a step further towards this common model, an algorithm for categorizing them was designed, enabling their discovery across different TAS. Thus, the main contributions of the thesis are: i) surveying the state of the art on task automation and coining the term Task Automation Service; ii) providing a semantic common model for describing TAS components and automations; iii) populating a categorized dataset of TAS components, used to learn ontologies of particular domains from the TAS perspective; and iv) designing an agent architecture for assisting users in setting up automations, that is aware of their context and acts in consequence.

Trust-ware: A Methodology to Analyze, Design, and Secure Trust and Reputation Systems

Entendemos por inteligencia colectiva una forma de inteligencia que surge de la colaboración y la participación de varios individuos o, siendo más estrictos, varias entidades. En base a esta sencilla definición podemos observar que este concepto es campo de estudio de las más diversas disciplinas como pueden ser la sociología, las tecnologías de la información o la biología, atendiendo cada una de ellas a un tipo de entidades diferentes: seres humanos, elementos de computación o animales. Como elemento común podríamos indicar que la inteligencia colectiva ha tenido como objetivo el ser capaz de fomentar una inteligencia de grupo que supere a la inteligencia individual de las entidades que lo forman a través de mecanismos de coordinación, cooperación, competencia, integración, diferenciación, etc. Sin embargo, aunque históricamente la inteligencia colectiva se ha podido desarrollar de forma paralela e independiente en las distintas disciplinas que la tratan, en la actualidad, los avances en las tecnologías de la información han provocado que esto ya no sea suficiente. Hoy en día seres humanos y máquinas a través de todo tipo de redes de comunicación e interfaces, conviven en un entorno en el que la inteligencia colectiva ha cobrado una nueva dimensión: ya no sólo puede intentar obtener un comportamiento superior al de sus entidades constituyentes sino que ahora, además, estas inteligencias individuales son completamente diferentes unas de otras y aparece por lo tanto el doble reto de ser capaces de gestionar esta gran heterogeneidad y al mismo tiempo ser capaces de obtener comportamientos aún más inteligentes gracias a las sinergias que los distintos tipos de inteligencias pueden generar. Dentro de las áreas de trabajo de la inteligencia colectiva existen varios campos abiertos en los que siempre se intenta obtener unas prestaciones superiores a las de los individuos. Por ejemplo: consciencia colectiva, memoria colectiva o sabiduría colectiva. Entre todos estos campos nosotros nos centraremos en uno que tiene presencia en la práctica totalidad de posibles comportamientos inteligentes: la toma de decisiones. El campo de estudio de la toma de decisiones es realmente amplio y dentro del mismo la evolución ha sido completamente paralela a la que citábamos anteriormente en referencia a la inteligencia colectiva. En primer lugar se centró en el individuo como entidad decisoria para posteriormente desarrollarse desde un punto de vista social, institucional, etc. La primera fase dentro del estudio de la toma de decisiones se basó en la utilización de paradigmas muy sencillos: análisis de ventajas e inconvenientes, priorización basada en la maximización de algún parámetro del resultado, capacidad para satisfacer los requisitos de forma mínima por parte de las alternativas, consultas a expertos o entidades autorizadas o incluso el azar. Sin embargo, al igual que el paso del estudio del individuo al grupo supone una nueva dimensión dentro la inteligencia colectiva la toma de decisiones colectiva supone un nuevo reto en todas las disciplinas relacionadas. Además, dentro de la decisión colectiva aparecen dos nuevos frentes: los sistemas de decisión centralizados y descentralizados. En el presente proyecto de tesis nos centraremos en este segundo, que es el que supone una mayor atractivo tanto por las posibilidades de generar nuevo conocimiento y trabajar con problemas abiertos actualmente así como en lo que respecta a la aplicabilidad de los resultados que puedan obtenerse. Ya por último, dentro del campo de los sistemas de decisión descentralizados existen varios mecanismos fundamentales que dan lugar a distintas aproximaciones a la problemática propia de este campo. Por ejemplo el liderazgo, la imitación, la prescripción o el miedo. Nosotros nos centraremos en uno de los más multidisciplinares y con mayor capacidad de aplicación en todo tipo de disciplinas y que, históricamente, ha demostrado que puede dar lugar a prestaciones muy superiores a otros tipos de mecanismos de decisión descentralizados: la confianza y la reputación. Resumidamente podríamos indicar que confianza es la creencia por parte de una entidad que otra va a realizar una determinada actividad de una forma concreta. En principio es algo subjetivo, ya que la confianza de dos entidades diferentes sobre una tercera no tiene porqué ser la misma. Por otro lado, la reputación es la idea colectiva (o evaluación social) que distintas entidades de un sistema tiene sobre otra entidad del mismo en lo que respecta a un determinado criterio. Es por tanto una información de carácter colectivo pero única dentro de un sistema, no asociada a cada una de las entidades del sistema sino por igual a todas ellas. En estas dos sencillas definiciones se basan la inmensa mayoría de sistemas colectivos. De hecho muchas disertaciones indican que ningún tipo de organización podría ser viable de no ser por la existencia y la utilización de los conceptos de confianza y reputación. A partir de ahora, a todo sistema que utilice de una u otra forma estos conceptos lo denominaremos como sistema de confianza y reputación (o TRS, Trust and Reputation System). Sin embargo, aunque los TRS son uno de los aspectos de nuestras vidas más cotidianos y con un mayor campo de aplicación, el conocimiento que existe actualmente sobre ellos no podría ser más disperso. Existen un gran número de trabajos científicos en todo tipo de áreas de conocimiento: filosofía, psicología, sociología, economía, política, tecnologías de la información, etc. Pero el principal problema es que no existe una visión completa de la confianza y reputación en su sentido más amplio. Cada disciplina focaliza sus estudios en unos aspectos u otros dentro de los TRS, pero ninguna de ellas trata de explotar el conocimiento generado en el resto para mejorar sus prestaciones en su campo de aplicación concreto. Aspectos muy detallados en algunas áreas de conocimiento son completamente obviados por otras, o incluso aspectos tratados por distintas disciplinas, al ser estudiados desde distintos puntos de vista arrojan resultados complementarios que, sin embargo, no son aprovechados fuera de dichas áreas de conocimiento. Esto nos lleva a una dispersión de conocimiento muy elevada y a una falta de reutilización de metodologías, políticas de actuación y técnicas de una disciplina a otra. Debido su vital importancia, esta alta dispersión de conocimiento se trata de uno de los principales problemas que se pretenden resolver con el presente trabajo de tesis. Por otro lado, cuando se trabaja con TRS, todos los aspectos relacionados con la seguridad están muy presentes ya que muy este es un tema vital dentro del campo de la toma de decisiones. Además también es habitual que los TRS se utilicen para desempeñar responsabilidades que aportan algún tipo de funcionalidad relacionada con el mundo de la seguridad. Por último no podemos olvidar que el acto de confiar está indefectiblemente unido al de delegar una determinada responsabilidad, y que al tratar estos conceptos siempre aparece la idea de riesgo, riesgo de que las expectativas generadas por el acto de la delegación no se cumplan o se cumplan de forma diferente. Podemos ver por lo tanto que cualquier sistema que utiliza la confianza para mejorar o posibilitar su funcionamiento, por su propia naturaleza, es especialmente vulnerable si las premisas en las que se basa son atacadas. En este sentido podemos comprobar (tal y como analizaremos en más detalle a lo largo del presente documento) que las aproximaciones que realizan las distintas disciplinas que tratan la violación de los sistemas de confianza es de lo más variado. únicamente dentro del área de las tecnologías de la información se ha intentado utilizar alguno de los enfoques de otras disciplinas de cara a afrontar problemas relacionados con la seguridad de TRS. Sin embargo se trata de una aproximación incompleta y, normalmente, realizada para cumplir requisitos de aplicaciones concretas y no con la idea de afianzar una base de conocimiento más general y reutilizable en otros entornos. Con todo esto en cuenta, podemos resumir contribuciones del presente trabajo de tesis en las siguientes. • La realización de un completo análisis del estado del arte dentro del mundo de la confianza y la reputación que nos permite comparar las ventajas e inconvenientes de las diferentes aproximación que se realizan a estos conceptos en distintas áreas de conocimiento. • La definición de una arquitectura de referencia para TRS que contempla todas las entidades y procesos que intervienen en este tipo de sistemas. • La definición de un marco de referencia para analizar la seguridad de TRS. Esto implica tanto identificar los principales activos de un TRS en lo que respecta a la seguridad, así como el crear una tipología de posibles ataques y contramedidas en base a dichos activos. • La propuesta de una metodología para el análisis, el diseño, el aseguramiento y el despliegue de un TRS en entornos reales. Adicionalmente se exponen los principales tipos de aplicaciones que pueden obtenerse de los TRS y los medios para maximizar sus prestaciones en cada una de ellas. • La generación de un software que permite simular cualquier tipo de TRS en base a la arquitectura propuesta previamente. Esto permite evaluar las prestaciones de un TRS bajo una determinada configuración en un entorno controlado previamente a su despliegue en un entorno real. Igualmente es de gran utilidad para evaluar la resistencia a distintos tipos de ataques o mal-funcionamientos del sistema. Además de las contribuciones realizadas directamente en el campo de los TRS, hemos realizado aportaciones originales a distintas áreas de conocimiento gracias a la aplicación de las metodologías de análisis y diseño citadas con anterioridad. • Detección de anomalías térmicas en Data Centers. Hemos implementado con éxito un sistema de deteción de anomalías térmicas basado en un TRS. Comparamos la detección de prestaciones de algoritmos de tipo Self-Organized Maps (SOM) y Growing Neural Gas (GNG). Mostramos como SOM ofrece mejores resultados para anomalías en los sistemas de refrigeración de la sala mientras que GNG es una opción más adecuada debido a sus tasas de detección y aislamiento para casos de anomalías provocadas por una carga de trabajo excesiva. • Mejora de las prestaciones de recolección de un sistema basado en swarm computing y odometría social. Gracias a la implementación de un TRS conseguimos mejorar las capacidades de coordinación de una red de robots autónomos distribuidos. La principal contribución reside en el análisis y la validación de las mejoras increméntales que pueden conseguirse con la utilización apropiada de la información existente en el sistema y que puede ser relevante desde el punto de vista de un TRS, y con la implementación de algoritmos de cálculo de confianza basados en dicha información. • Mejora de la seguridad de Wireless Mesh Networks contra ataques contra la integridad, la confidencialidad o la disponibilidad de los datos y / o comunicaciones soportadas por dichas redes. • Mejora de la seguridad de Wireless Sensor Networks contra ataques avanzamos, como insider attacks, ataques desconocidos, etc. Gracias a las metodologías presentadas implementamos contramedidas contra este tipo de ataques en entornos complejos. En base a los experimentos realizados, hemos demostrado que nuestra aproximación es capaz de detectar y confinar varios tipos de ataques que afectan a los protocoles esenciales de la red. La propuesta ofrece unas velocidades de detección muy altas así como demuestra que la inclusión de estos mecanismos de actuación temprana incrementa significativamente el esfuerzo que un atacante tiene que introducir para comprometer la red. Finalmente podríamos concluir que el presente trabajo de tesis supone la generación de un conocimiento útil y aplicable a entornos reales, que nos permite la maximización de las prestaciones resultantes de la utilización de TRS en cualquier tipo de campo de aplicación. De esta forma cubrimos la principal carencia existente actualmente en este campo, que es la falta de una base de conocimiento común y agregada y la inexistencia de una metodología para el desarrollo de TRS que nos permita analizar, diseñar, asegurar y desplegar TRS de una forma sistemática y no artesanal y ad-hoc como se hace en la actualidad. ABSTRACT By collective intelligence we understand a form of intelligence that emerges from the collaboration and competition of many individuals, or strictly speaking, many entities. Based on this simple definition, we can see how this concept is the field of study of a wide range of disciplines, such as sociology, information science or biology, each of them focused in different kinds of entities: human beings, computational resources, or animals. As a common factor, we can point that collective intelligence has always had the goal of being able of promoting a group intelligence that overcomes the individual intelligence of the basic entities that constitute it. This can be accomplished through different mechanisms such as coordination, cooperation, competence, integration, differentiation, etc. Collective intelligence has historically been developed in a parallel and independent way among the different disciplines that deal with it. However, this is not enough anymore due to the advances in information technologies. Nowadays, human beings and machines coexist in environments where collective intelligence has taken a new dimension: we yet have to achieve a better collective behavior than the individual one, but now we also have to deal with completely different kinds of individual intelligences. Therefore, we have a double goal: being able to deal with this heterogeneity and being able to get even more intelligent behaviors thanks to the synergies that the different kinds of intelligence can generate. Within the areas of collective intelligence there are several open topics where they always try to get better performances from groups than from the individuals. For example: collective consciousness, collective memory, or collective wisdom. Among all these topics we will focus on collective decision making, that has influence in most of the collective intelligent behaviors. The field of study of decision making is really wide, and its evolution has been completely parallel to the aforementioned collective intelligence. Firstly, it was focused on the individual as the main decision-making entity, but later it became involved in studying social and institutional groups as basic decision-making entities. The first studies within the decision-making discipline were based on simple paradigms, such as pros and cons analysis, criteria prioritization, fulfillment, following orders, or even chance. However, in the same way that studying the community instead of the individual meant a paradigm shift within collective intelligence, collective decision-making means a new challenge for all the related disciplines. Besides, two new main topics come up when dealing with collective decision-making: centralized and decentralized decision-making systems. In this thesis project we focus in the second one, because it is the most interesting based on the opportunities to generate new knowledge and deal with open issues in this area, as well as these results can be put into practice in a wider set of real-life environments. Finally, within the decentralized collective decision-making systems discipline, there are several basic mechanisms that lead to different approaches to the specific problems of this field, for example: leadership, imitation, prescription, or fear. We will focus on trust and reputation. They are one of the most multidisciplinary concepts and with more potential for applying them in every kind of environments. Besides, they have historically shown that they can generate better performance than other decentralized decision-making mechanisms. Shortly, we say trust is the belief of one entity that the outcome of other entities’ actions is going to be in a specific way. It is a subjective concept because the trust of two different entities in another one does not have to be the same. Reputation is the collective idea (or social evaluation) that a group of entities within a system have about another entity based on a specific criterion. Thus, it is a collective concept in its origin. It is important to say that the behavior of most of the collective systems are based on these two simple definitions. In fact, a lot of articles and essays describe how any organization would not be viable if the ideas of trust and reputation did not exist. From now on, we call Trust an Reputation System (TRS) to any kind of system that uses these concepts. Even though TRSs are one of the most common everyday aspects in our lives, the existing knowledge about them could not be more dispersed. There are thousands of scientific works in every field of study related to trust and reputation: philosophy, psychology, sociology, economics, politics, information sciences, etc. But the main issue is that a comprehensive vision of trust and reputation for all these disciplines does not exist. Every discipline focuses its studies on a specific set of topics but none of them tries to take advantage of the knowledge generated in the other disciplines to improve its behavior or performance. Detailed topics in some fields are completely obviated in others, and even though the study of some topics within several disciplines produces complementary results, these results are not used outside the discipline where they were generated. This leads us to a very high knowledge dispersion and to a lack in the reuse of methodologies, policies and techniques among disciplines. Due to its great importance, this high dispersion of trust and reputation knowledge is one of the main problems this thesis contributes to solve. When we work with TRSs, all the aspects related to security are a constant since it is a vital aspect within the decision-making systems. Besides, TRS are often used to perform some responsibilities related to security. Finally, we cannot forget that the act of trusting is invariably attached to the act of delegating a specific responsibility and, when we deal with these concepts, the idea of risk is always present. This refers to the risk of generated expectations not being accomplished or being accomplished in a different way we anticipated. Thus, we can see that any system using trust to improve or enable its behavior, because of its own nature, is especially vulnerable if the premises it is based on are attacked. Related to this topic, we can see that the approaches of the different disciplines that study attacks of trust and reputation are very diverse. Some attempts of using approaches of other disciplines have been made within the information science area of knowledge, but these approaches are usually incomplete, not systematic and oriented to achieve specific requirements of specific applications. They never try to consolidate a common base of knowledge that could be reusable in other context. Based on all these ideas, this work makes the following direct contributions to the field of TRS: • The compilation of the most relevant existing knowledge related to trust and reputation management systems focusing on their advantages and disadvantages. • We define a generic architecture for TRS, identifying the main entities and processes involved. • We define a generic security framework for TRS. We identify the main security assets and propose a complete taxonomy of attacks for TRS. • We propose and validate a methodology to analyze, design, secure and deploy TRS in real-life environments. Additionally we identify the principal kind of applications we can implement with TRS and how TRS can provide a specific functionality. • We develop a software component to validate and optimize the behavior of a TRS in order to achieve a specific functionality or performance. In addition to the contributions made directly to the field of the TRS, we have made original contributions to different areas of knowledge thanks to the application of the analysis, design and security methodologies previously presented: • Detection of thermal anomalies in Data Centers. Thanks to the application of the TRS analysis and design methodologies, we successfully implemented a thermal anomaly detection system based on a TRS.We compare the detection performance of Self-Organized- Maps and Growing Neural Gas algorithms. We show how SOM provides better results for Computer Room Air Conditioning anomaly detection, yielding detection rates of 100%, in training data with malfunctioning sensors. We also show that GNG yields better detection and isolation rates for workload anomaly detection, reducing the false positive rate when compared to SOM. • Improving the performance of a harvesting system based on swarm computing and social odometry. Through the implementation of a TRS, we achieved to improve the ability of coordinating a distributed network of autonomous robots. The main contribution lies in the analysis and validation of the incremental improvements that can be achieved with proper use information that exist in the system and that are relevant for the TRS, and the implementation of the appropriated trust algorithms based on such information. • Improving Wireless Mesh Networks security against attacks against the integrity, confidentiality or availability of data and communications supported by these networks. Thanks to the implementation of a TRS we improved the detection time rate against these kind of attacks and we limited their potential impact over the system. • We improved the security of Wireless Sensor Networks against advanced attacks, such as insider attacks, unknown attacks, etc. Thanks to the TRS analysis and design methodologies previously described, we implemented countermeasures against such attacks in a complex environment. In our experiments we have demonstrated that our system is capable of detecting and confining various attacks that affect the core network protocols. We have also demonstrated that our approach is capable of rapid attack detection. Also, it has been proven that the inclusion of the proposed detection mechanisms significantly increases the effort the attacker has to introduce in order to compromise the network. Finally we can conclude that, to all intents and purposes, this thesis offers a useful and applicable knowledge in real-life environments that allows us to maximize the performance of any system based on a TRS. Thus, we deal with the main deficiency of this discipline: the lack of a common and complete base of knowledge and the lack of a methodology for the development of TRS that allow us to analyze, design, secure and deploy TRS in a systematic way.